DESCRIPTION:(provided by applicant) The long term objective of this research is to understand the biophysical mechanisms by which time varying sensory stimuli are integrated in individual neurons. The immediate goal is to provide detailed biophysical explanations of how individual neurons multiply two inputs and how they implement invariance to certain stimulus attributes. Multiplication has been implicated in many neural computations, like the extraction of motion information from visual images, in both vertebrate and invertebrate nervous systems. Invariance is an attribute commonly found in higher order neurons that respond selectively to a stimulus feature independently of its context. Currently, there is little understanding of how these computations are accomplished by neurons. These issues will be investigated in the visual system of the locust, which possesses a neuron, the lobula giant movement detector (LGMD), that responds to objects looming on a collision course towards the animal. This neuron implements a multiplication operation between two distinct inputs impinging on its dendrites and exhibits responses that are invariant to many attributes of the looming object. Many features of LGMD make it a favorable subject for biophysical studies. The specific aims of the project are to characterize the time-course of activation of the two inputs impinging on LGMD and their integration within its dendritic tree. Additionally, the anatomy of the cell, its intrinsic membrane properties and the geometry of the eye will be characterized and used to build a model of the cell and its response to looming stimuli. The techniques applied will include multielectrode recordings from presynaptic neurons to LGMD, intracellular recordings, pharmacological manipulations and compartmental modeling. The model and experimental data will be used to identify the biophysical mechanisms underlying multiplication and invariance in this neuron. Because similar computations are found in vertebrate central nervous systems, this project is expected to advance the general understanding of how multiplication and invariance are implemented for neural information processing.